Roy, Anthony M. (2010) Neuro-evolution using recombinational algorithms and embryogenesis for robotic control. Dissertation (Ph.D.), California Institute of Technology. http://resolver.caltech.edu/CaltechTHESIS:06092010-140839602
Control tasks involving dramatic nonlinearities, such as decision making, can be challenging for classical design methods. However, autonomous, stochastic design methods such as evolutionary computation have proved effective. In particular, genetic algorithms that create designs via the application of recombinational rules are robust and highly scalable. Neuro-Evolution Using Recombinational Algorithms and Embryogenesis (NEURAE) is a genetic algorithm that creates C++ programs that in turn create neural networks which can function as logic gates. The neural networks created are scalable and robust enough to feature redundancies that allow the network to function despite internal failures. An analysis of NEURAE evinces how biologically inspired phenomena apply to simulated evolution. This allows for an optimization of NEURAE that enables it to create controllers for a simulated swarm of Khepera-inspired robots.
|Item Type:||Thesis (Dissertation (Ph.D.))|
|Subject Keywords:||Genetic Algorithm, Neural Network, Robotics, Genetic Programming, Artificial Intelligence|
|Degree Grantor:||California Institute of Technology|
|Division:||Engineering and Applied Science|
|Major Option:||Mechanical Engineering|
|Thesis Availability:||Public (worldwide access)|
|Defense Date:||11 December 2009|
|Default Usage Policy:||No commercial reproduction, distribution, display or performance rights in this work are provided.|
|Deposited By:||Anthony Roy|
|Deposited On:||04 Aug 2010 17:57|
|Last Modified:||13 Apr 2015 18:58|
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